skills/light-review/SKILL.md
Lightweight code review. Dispatches to OpenAI Codex CLI (/codex-review) by default, or to Claude depending on flags. Use when: (1) Quick review of a small change, (2) Child agents self-reviewing before reporting to manager, (3) User says 'light review' or 'quick review', (4) Review is needed but /deep-review is overkill. Always operates in PR/diff mode.
npx skillsauth add takazudo/claude-resources light-reviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Lightweight code review. Runs whichever reviewers are specified by flags; falls back to the skill's defaults when none are passed.
On Claude Code on the web (
$CLAUDE_CODE_REMOTE=true): followweb/web-mode.md. This is Claude-only — the default/codex-reviewbackend is unavailable, so default to a Claude reviewer and ignore-co. Read the PR diff and raise anyagent-foundissues via the GitHub MCP, notgh.
Subagent safety: when this skill runs inside a subagent (a worktree child, a team member, or any agent instructed to review in the foreground), every backend must execute in blocking/foreground form — a subagent that backgrounds a review and then waits for a completion notification parks forever, because that notification is only delivered to the parent/manager session.
-cobackend: safe automatically.Skill(skill="codex-review")executes on the same invoking agent — so it inherits that agent's context, and codex-review's own "Subagent / child-agent context (MANDATORY)" rule takes over, running codex as a single foreground Bash call instead of a background task. Nothing extra to do here.- Claude branch (
-haiku/-so/-op,code-reviewersubagents): already park-safe —Agentcalls block and return synchronously to the caller, so there's no background task and no notification to await, in a subagent or anywhere else. Reviewer count is context-scoped, though: the manager/interactive context spawns 2code-reviewersubagents, but when this skill itself runs inside a subagent/child agent it spawns only ONE (not 2) — same 6-concurrent CPU-budget rationale as codex-review's child-context fallback (up to 6 children × 2 nested reviewers would blow the manager's 6-concurrent budget). Blocking/synchronous behavior is unchanged either way.
-haiku / --haiku — Claude Haiku-so / --sonnet — Claude Sonnet-op / --opus — Claude OpusIf none passed and no backend flag is passed either, the skill falls to the backend default (-co) — no Claude reviewers run.
If a model flag IS passed, it turns on the Claude-reviewers branch (2 code-reviewer subagents at that model in the manager/interactive context; 1 in a subagent/child-agent context — see Step 2).
If multiple model flags are passed, the last one wins.
-co / --codex — OpenAI Codex CLI (/codex-review)Multiple backend flags may be combined — each specified backend runs in parallel and findings are consolidated.
Default for this skill: -co (used when neither a model flag nor any backend flag is passed).
| Flags passed | What runs |
|---|---|
| (none) | /codex-review only |
| -op (or -so, -haiku) | 2 Claude reviewers at that model (1 in a subagent/child-agent context) |
| -co | /codex-review only |
BRANCH=$(git branch --show-current)
BASE=$(gh pr view --json baseRefName -q '.baseRefName' 2>/dev/null)
If no PR exists, use the default branch:
BASE=$(git remote show origin | grep 'HEAD branch' | awk '{print $NF}')
git diff "$BASE"...HEAD
Based on the flags, launch every selected reviewer in the same message (parallel).
Reviewer count is context-scoped (see the "Subagent safety" note at the top of this file):
code-reviewer subagents (Reviewer 1 + Reviewer 2 below) with model set to haiku / sonnet / opus per the model flag.code-reviewer subagent at that model, and give it both focus lists below (Bugs & Logic + Quality & Structure) so a single reviewer covers the full scope. 6 concurrent children × 2 nested reviewers would blow the manager's 6-concurrent budget — 1 per child keeps it affordable.Either way the Agent calls block and return synchronously, so there is no background task and no parking risk.
Reviewer 1: Bugs & Logic
Review the code changes focusing on:
1. Logic errors, typos, incorrect implementations
2. Missing null checks, off-by-one errors
3. Broken functionality, incorrect API usage
4. Error handling issues
Be concise. Only flag real problems, not style preferences.
REPORTING: Save your FULL findings to the log file (as per your log generation rules).
Then return to the caller ONLY:
- A bullet list of high-priority findings (1 sentence each, max 3 items)
- The log file path
Do NOT return the full analysis — it is in the log file.
Reviewer 2: Quality & Structure
Review the code changes focusing on:
1. Code duplication (DRY violations)
2. Overly complex code that can be simplified
3. Type safety issues
4. Performance concerns (unnecessary re-renders, missing memoization)
5. Better patterns or abstractions
Be concise. Only flag real problems, not style preferences.
REPORTING: Save your FULL findings to the log file (as per your log generation rules).
Then return to the caller ONLY:
- A bullet list of high-priority findings (1 sentence each, max 3 items)
- The log file path
Do NOT return the full analysis — it is in the log file.
For each specified backend, invoke the matching skill in parallel (single message, multiple tool calls):
-co → Skill(skill="codex-review") — silently falls back to Opus (2 code-reviewer subagents at model: opus) if codex is rate-limitedEach backend skill already handles its own rate-limit / fallback behavior silently. For -co, that fallback is Opus — the user picked -co to mean "the better reviewer," and Opus is the Claude-side stand-in when codex is down.
Equivalent to -co. Invoke /codex-review only.
CRITICAL: Launch all reviewers (Claude + backend) in parallel in a single message.
After all reviewers complete (each returns high-priority items + log path):
If fixes were applied, commit them with a descriptive message.
/deep-review instead./logrefer for future sessions.tools
Acceptance gate for a branch produced by an OpenAI Codex CLI run — usually Codex implementing a /big-plan epic that was handed off to it. Codex reports the work 'done' (or the user flags it WIP with corrections); this skill confirms the branch actually fulfils the original spec, fixes what falls short, and routes larger discoveries into GitHub issues. Use when: (1) User says '/finalize-codex-work', 'finalize codex work', 'confirm the codex work', 'check the codex branch', or 'codex said it's done', (2) A branch is the result of a Codex CLI session and needs verification against its spec issue/PR, (3) After assigning a /big-plan epic to Codex CLI. Pass -m/--merge to run /pr-complete -c at the end.
tools
Read a Figma design node directly from a share URL via the Figma REST API — no Dev Mode subscription, no MCP, no desktop app. Renders the node to PNG and dumps its full style/layout JSON so the design can be described, compared, or implemented. Use whenever the user gives a Figma design URL (figma.com/design/... or /file/...) and wants to see, read, inspect, reference, or implement that node — including `/fig-url-refer <url>`. This is the URL-based counterpart to `/figrefer` (which needs a Dev-plan desktop MCP); prefer this one when the input is a URL rather than a live desktop selection.
tools
Sync the user's Claude Code workflow skills into the OpenAI Codex CLI settings repo ($HOME/.codex) as Codex-native ports, fix the Codex .gitignore for new local state, then commit and push. Use when: (1) user says '/dev-codex-sync-settings-from-claude', 'sync codex settings', 'sync claude skills to codex', 'port skills to codex', or 'update codex from claude'; (2) after updating ~/.claude workflow skills (big-plan, x, x-as-pr, x-wt-teams) and Codex should catch up; (3) the $HOME/.codex repo has drifted behind $HOME/.claude. The ports are condensed Codex-native REWRITES, never file copies.
development
Analyze a video file (mov, mp4, webm, etc.) or a YouTube video by extracting still frames with ffmpeg and reading them chronologically with vision — Claude cannot ingest video files directly. Use whenever the user provides a video file path or YouTube URL and wants to know what happens in it: "read this video", "watch this video", "check this recording", "what happens in this .mov/.mp4", analyzing a screen recording of a UI bug, or verifying UI behavior captured in a video, even if they don't name this skill.